Futures hedging using clusters with dynamic behavior of market fluctuation

Yu Chia Hsu*, An-Pin Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this study, a novel procedure of time series dynamic behaviors clustering is proposed to improve the accuracy of minimum -variance optimal hedge ratio (OHR) estimation for future hedging. The dynamic behaviors of market fluctuation are extracted by measurement of variances, covariance, price spread, and their first and second differences. The behaviors with similar patterns are clustered using a growing hierarchical self-organizing map (GHSOM). The observations for OHR estimation are collected based on the hierarchical cluster structure and processed by within-cluster resampling. The spots and futures of the Taiwan Weighted Index (TWI) are adopted to demonstrate that the futures hedge effectiveness can be significantly improved.

Original languageEnglish
Title of host publication2012 International Joint Conference on Neural Networks, IJCNN 2012
DOIs
StatePublished - 22 Aug 2012
Event2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012 - Brisbane, QLD, Australia
Duration: 10 Jun 201215 Jun 2012

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Part of the 2012 IEEE World Congress on Computational Intelligence, WCCI 2012
Country/TerritoryAustralia
CityBrisbane, QLD
Period10/06/1215/06/12

Keywords

  • GHSOM
  • cluster analysis
  • financial time series
  • hedge ratio

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